{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression\n",
    "This notebook will start by covering what logistic regression is, how it works, and how to use logistic regression in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data\n",
    "\n",
    "The data we will use is the Breast Cancer Wisconsin (Diagnostic) Data Set: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic) which I converted to a csv for convenience. The goal of this prediction is successfully classifying cancer as malignant (1) or benign (0). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/wisconsinBreastCancer.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_radius</th>\n",
       "      <th>mean_texture</th>\n",
       "      <th>mean_perimeter</th>\n",
       "      <th>mean_area</th>\n",
       "      <th>mean_smoothness</th>\n",
       "      <th>mean_compactness</th>\n",
       "      <th>mean_concavity</th>\n",
       "      <th>mean_concave_points</th>\n",
       "      <th>mean_symmetry</th>\n",
       "      <th>mean_fractal_dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst_texture</th>\n",
       "      <th>worst_perimeter</th>\n",
       "      <th>worst_area</th>\n",
       "      <th>worst_smoothness</th>\n",
       "      <th>worst_compactness</th>\n",
       "      <th>worst_concavity</th>\n",
       "      <th>worst_concave_points</th>\n",
       "      <th>worst_symmetry</th>\n",
       "      <th>worst_fractal_dimension</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_radius  mean_texture  mean_perimeter  mean_area  mean_smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean_compactness  mean_concavity  mean_concave_points  mean_symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean_fractal_dimension  ...  worst_texture  worst_perimeter  worst_area  \\\n",
       "0                 0.07871  ...          17.33           184.60      2019.0   \n",
       "1                 0.05667  ...          23.41           158.80      1956.0   \n",
       "2                 0.05999  ...          25.53           152.50      1709.0   \n",
       "3                 0.09744  ...          26.50            98.87       567.7   \n",
       "4                 0.05883  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   worst_smoothness  worst_compactness  worst_concavity  worst_concave_points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst_symmetry  worst_fractal_dimension  diagnosis  \n",
       "0          0.4601                  0.11890          1  \n",
       "1          0.2750                  0.08902          1  \n",
       "2          0.3613                  0.08758          1  \n",
       "3          0.6638                  0.17300          1  \n",
       "4          0.2364                  0.07678          1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Relationship between worst_concave_points and diagnosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'worst_concave_points')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['worst_concave_points'], df['diagnosis'])\n",
    "plt.ylabel('malignant (1) or benign (0)', fontsize = 12)\n",
    "plt.xlabel('worst_concave_points', fontsize = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the name Logistic <b> Regression</b>\n",
    "Linear regression was good when we wanted to predict a continuous value. This section is just showing trying using linear regression to classify and see where it falls short. malignant (1 in the graph above) or benign (0 in the graph below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['worst_concave_points'].values.reshape(-1,1)\n",
    "y = df['diagnosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'worst_concave_points')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T6WRmw4FLgWsTi0xEitP8+dCvX6rcuzcsXFi4eKSoxWrJuPto4OfAccA84BTgV+5+X4KxiUixGTIkM8G89ZYSjDQpdq+cu48DxiUViJkNBW4GyoG73P3aBtsvAk6Mip2AgcA27v6Jmc0BVgG1wAZ3r0oqTpEOado02GuvVPnrX4fnny9cPFIyWjLV/+mEiTL7AAuBB4HR7p51xcyWMLNy4DbCaLX5wGtm9qi7v1u/j7tfD1wf7X8M8FN3/yTtYYa4+9LWxiIiDfTrFy6R1Zs7F/r3L1w8UlJiXS4zs98BFwNjgYuinxcC1+UpjkHATHef5e7rCQlsWBP7Dyd8b0dEkvLCC6ETvz7BnHxy6NhXgpEWiNuSORXYz903/jtjZuOB1wl9Na3Vl9DXU28+MDjbjmbWDRgKnJdW7cBT0fDqO9x9VI5jRwAjAPrrD0Uku2wTWi5b1nioskgMcYcwr4puDes+zVMc2cY85roMdwzwUoNLZQe6+37AEcC5ZnZQtgPdfZS7V7l71TbbbNO6iEXao7FjMxPMZZeFpKMEI5soZ0vGzHZMK/4BGGtm1xJaGf0Il81uylMc9Y9ZbwdCv082J9DgUpm7L4x+LjazhwmX39QrKRLXhg1QUZFZt2ZN42liRFqoqZZM/VT+MwmjvoYAE4FpwJPAoVF9PrwG7GJmA8ysMyGRPNpwp2iGgYOBR9LqupvZFvX3gW8B7+QpLpH277bbMhPMyJGh9aIEI3mQsyXTltP7u/sGMzuPkMTKCaPWppnZ2dH2kdGu3wGecvfVaYdvBzxs4VvGnYD73f3JtopdpGStWQPdu2fWbdgA5eWFiUfaJcvDCOSSVFVV5dXV1YUOQ6QwLr0UrrkmVR43DoY1NaBTJDCzKS35LqKmSBXpSJYta7wqpSa0lARpxUuRjuLEEzMTzIsvakJLSVzcRcvK3L0u6WBEJAFz50JlZapcWQmzZxcqGulgmm3JRFO+rDazLm0Qj4jk0wEHZCaYd99VgpE21WyScfda4H2gZ/LhiEhevPlmuAz2yiuh/M1vhktjAwcWNi7pcOJ2/N9HWE/mZsIXJzcOSXP3fyURmIhsom22gaVpc8XOnw99+xYuHunQ4iaZc6KfVzSod2BHRKTw/v1v+MY3UuUzz4RRWafxE2kzsZKMuw9IOhAR2UTZJrRcsQK23LIg4Yikiz2E2cw6mdlBZjbczL5uZvqOjUih/fWvmQnmqqtC0lGCkSIRdwjz7sBjQFfClPz9gM/N7Bh3n55gfCKSTU0NdO6cWff559BFg0CluMRtyfwRGAX0c/evuvsOwMioXkTa0h/+kJlg7r47tF6UYKQIxb3ktQ9wWIOllv8A/DLfAYlIDqtXw+abZ9bV1jbujxEpInHfnQsJU+yn+zq513wRkXy64ILMBDN+fPYOf5EiE7clcynwaLTk8lzgi8BRwElJBSYiwJIlsO22qXJ5eeiP0XxjUiJi/Rvk7o8C+xEWA9si+vkVd3+kyQNFZNMdd1xmgpk0Kaz3ogQjJST2MGR3fx/43wRjERGAWbNgp51S5YEDw5xjIiVIF3RFislXvpKZYN57TwlGSpqSjEgxeP31cBns9ddD+eijQ8f+rrsWNi6RVtK39kUKbYst4LPPUuVFi2D77QsXj0geqSUjUihPPRVaL/UJ5txzQ+tFCUbakSZbMtH8ZMcShivvDfQAVgBvAk8A49x9Qz4CMbOhwM1AOXCXu1/bYPshwCNA/YpLY939qjjHihSVurowFDndypXwhS8UJh6RBOVsyZjZWcAs4CzgA+Bq4Ozo5wfAmcAsMzu7tUFEq2/eBhwB7AEMN7M9suz6grvvE92uauGxIoX3l79kJphrrw2tFyUYaaeaasnsCgxy94+ybHsY+K2Z9QYuyEMcg4CZ7j4LwMweBIYBcYbVtOZYkbaxfn3jucXWrWs8yaVIO5OzJePuF+RIMOn7LHL3C/MQR1/C7M715kd1DX3VzN40syfMbM8WHouZjTCzajOrXrJkSR7CFonh+uszE8y994bWixKMdACtGl1mZv3d/cM8xJHtK8zeoPw68EV3/8zMjgTGAbvEPDZUuo8izCZNVVVV1n1E8mbVqsaXwTShpXQwm/xuN7MupDrhW2s+YY2aejvQYPJNd//U3T+L7k8AKsysV5xjRdrc+ednJpgnn9SEltIhNTe67KAmNudz8YrXgF3MbACwADgB+GGDWLYHPnZ3N7NBhAS5jDDarcljRdrMRx9B796pcrduYYp+kQ6quctlzwKLgLokg3D3DWZ2HjCRMAx5tLtPqx+55u4jge8D55jZBmAtcEK0vk3WY5OMVySrY4+Fxx5LlaurwzQxIh2YZa5D1mCj2WzgRHd/Ocu2zYDV7l7e+MjiV1VV5dXV1YUOQ9qD//wnc/qXvfeGN94oWDgiSTKzKe5eFXf/5i4QVwO5HqwOyEenv0jp2mOPzAQzc6YSjEia5pLMD4Hbs21w9/XuPiD/IYmUgFdfDVPCTJ8eyt/9bujYT59BWUSa7pNx95q2CkSkJNR/v2VD2mxKH3+cubiYiGzU1LQy50fDlHMysy5mdn7+wxIpQhMmhCHI9QnmZz8LSUcJRiSnploy2wMzzWwC8BzwHrCKsPzyrsAhhPnC7k04RpHCyjah5apVsPnmhYlHpIQ0Na3MpcC+wH+AHxNmXX4HmACcDswA9nX3y9ogTpHC+NOfMhPMjTeG1osSjEgszfXJLAVuiG4iHce6dbDZZpl169dDRUVh4hEpUZrjQqShq6/OTDAPPBBaL0owIi2m5ZdF6q1cCT16ZNbV1YWhyiKySdSSEQE466zMBPPMM6H1ogQj0ipqyUjHtnAh9E1bfmjrrWHZssLFI9LOxGrJmNnUHPWa/EtK1+GHZyaYqVOVYETyLG5LZueGFWZmwI75DUekDUyfHuYcqzd4MEyaVLh4RNqx5taTqf+iZee0+/UqAU2pL6Vlp51g1qxUefZsqKwsWDgi7V1zLZkPctx34CXg73mPSCQJL78MBx6YKg8fDvffX7h4RDqI5r6MeSWAmU1y94ltE5JIHmVb8njpUujZszDxiHQwsTr+3X2ime1mZseb2enpt6QDFNlkjz6amWAuuSQkHSUYkTYTq+PfzC4FLgfeBNakbXJgdAJxiWy62lro1OCtvXo1dOtWmHhEOrC4X8b8H2CQuw929yFpt28kGJtIy40alZlgbrkltF6UYEQKIu4Q5rWEWZdFitPnn0PXrpl1NTWNWzQi0qbitmR+BdxiZr3NrCz9lq9AzGyomb1nZjPN7JIs2080s7ei28tmtnfatjlm9raZvaEviHZAv/51ZoJ56KHQelGCESm4uH+F90Q/z0irM0KfTHmjvVvIzMqB24DDgPnAa2b2qLu/m7bbbOBgd19uZkcAo4DBaduHREsTSEexfHmYBiadJrQUKSpxWyIDotuOabf6cj4MAma6+yx3Xw88CAxL38HdX3b35VFxErBDnp5bStFpp2UmmOee04SWIkUoVkvG3ecmHEdfYF5aeT6ZrZSG6lfqrOfAU2bmwB3uPirbQWY2AhgB0L9//1YFLAUybx6kv3Z9+sCCBYWLR0SaFPuitZkdCxwM9CJcKgPA3U/OQxzZ/v30HHEMISSZr6VVH+juC81sW+BpM5vh7s83esCQfEYBVFVVZX18KWIHHwzPp72sb78Ne+1VuHhEpFlxZ2H+NXBHtP9xwDLgcGBFnuKYD/RLK+8ALMwSx5eBu4Bh7r5xulx3Xxj9XAw8TLj8Ju3FO++Ey2D1Ceagg8KlMSUYkaIXt0/mdOAwd/8psD76eQxhksx8eA3YxcwGmFln4ATg0fQdzKw/MBb4kbu/n1bf3cy2qL8PfAt4J09xSaH17Qtf+lKq/OGHof9FREpC3CTTw93rP7jXm1mFu79KuHzWau6+ATgPmAhMB/7m7tPM7GwzOzva7XKgJ/DHBkOVtwNeNLM3gVeBx939yXzEJQX0/POh9bIwatCeckpovfTr1/RxIlJU4vbJfGBme7r7NEIr4RwzWw4sb+a42Nx9AjChQd3ItPtnkDmEur5+FrB3w3opUdkmtPzkE9hqq8LEIyKtErclcxmhFQHwC+B84HrgZ0kEJR3UP/6RmWB+9auQdJRgREpW3CHME9LuTybLSpkim2zDBqioyKxbs6bxNDEiUnJiTwtjZlua2SAz+0b6LcngpAO49dbMBDNyZGi9KMGItAtxp/o/lTDty2c0nuo/X9/6l45kzRro3j2zbsMGKG/1LEUiUkTitmSuBr7v7tu5+4C0mxKMtNwvfpGZYMaNC60XJRiRdifu6LJOwFNJBiIdwLJl0KtXZp0mtBRp1+K2ZK4DLsvn1P7SwQwfnplgXnpJE1qKdABxWzI/BbYHfm5my9I3uLtmmpTc5syBAQNS5QEDYNasgoUjIm0rbpI5KdEopH3af3+YPDlVfvddGDiwcPGISJuL+z0ZTRYl8b35JuyzT6p82GHwlLr0RDqiuEOYr8qxaR1hBuUn3f3jvEUlpatnzzANTL0FC8KaLyLSIcXtyN8VuBgYQvi2/5CovC9wDjDLzIYmEqGUhmeeCZ349QlmxIjQsa8EI9Khxe2TKQNOcPeH6yvMbBjwQ3ff38xOAa4FNPtxR5NtQssVK2DLLQsSjogUl7gtmcNpsL4LMB44Irr/F2CnfAUlJeLBBzMTzG9+E5KOEoyIRGJP9U+4LHZrWt3ZUT2EJZlX5zEuKWY1NdC5c2bd559Dly6FiUdEilbclswZwIVmNs/MJpnZPOAi4MfR9t2AXyURoBSZm27KTDCjR4fWixKMiGQRdwjz62a2C/BVoDewCHjF3Wui7c8DzycWpRTeZ5/BFltk1tXWNu6PERFJE/sTwt1r3P15d/9r9LMmycCkiPzsZ5kJ5vHHs3f4i4g0kLMlY2bT3X1gdH8eYVr/RjStTDu2eDFst12q3KkTrF+v+cZEJLamLpedmXZf08p0NN/7HowdmypPngyDBhUuHhEpSTmTjLu/mHY/8Wlloi9z3gyUA3e5+7UNtlu0/UjCwmmnuvvrcY7Nl8pLHm9UN+faozLK46Yu4MrHprF8TeOrid07l7N6fS1m4WpTMeq/fBHPj0r9f/F+z/5864w/wtglMLbx+bd3Ro4mfBuoKIPuXSpYsbaGcjNq3enboysXHb4b1XM/4f7JH1LnmfuuXFtDn2ifb+/bN+djj5u6gOsnvsfCFWvp06MrQ3bfhn/PWLKx3NzxInGZ5/i0a2IqmQzufnmrgzArB94HDiNMU/MaMNzd303b50jgvwlJZjBws7sPjnNsNlVVVV5dXR07xmwJpl59ohk3dQEXPfQmNbVFmkGa8fifzmfPxakZkg858w7mbK0PmmJTBtQ1s0/XinKu+e6XsiaKcVMX8Iuxb7O2pnaTjpeOzcymuHtV3P2b6rntF/OWD4OAme4+y93XAw8CwxrsMwy414NJQA8z6x3z2DZx/cT3SjLB7PXRTOZcd/TGBPP0zoOovHi8EkyRai7BAKytqeX6ie9l3Xb9xPeaTDDNHS/SEk1dLjutDePoC8xLK88ntFaa26dvzGMBMLMRwAiA/v3zP15h4Yq1eX/MpL174/foVrNuY/m/zh3Dks23KmBEki+53o9x36el+H6W4tOiMahmtoWZDTCzHetveYoj23Clhk2CXPvEOTZUuo9y9yp3r9pmm21aGGLz+vTomvfHTMpBs6Yw57qjNyaYP+93FJUXj1eCaUdyvR/jvk9L6f0sxSvuVP97APcBe5P6YK//IC/PQxzzybz0tgOwMOY+nWMc2yYuOny3ou+TMa9j9u+Ozajb83/+xuou3QoUkbRU3D6Ziw7fLeu2iw7fLVafTK7jRVoibkvmj8C/ga2BT4GtgDuAU/IUx2vALlErqTNwAo0n5HwUONmC/YGV7r4o5rGt1nAUWbb6b+/bl+u/vzdbdavIum/3ziEfF+prJt95518ZCeaaQ06l8uLxSjBNKOQ3girKoEfX8F4qj940fXt05cYf7MNJ+/enzBrva9E+TXXaf3vfvlzz3S/Rt0fXjfuftH//jLI6/SVfco4uy9jJbDmwrbvXmNkKd+9hZt2Bd9x9QHPHxwokjB77A6FlNNrdrzazswHcfWQ0hPlWYChhCPNp7l6d69jmnq+lo8tK2vr1jecWW7eu8SSXIiLNaOnosrizMH8OVAA1wFIz6w8sB3q2PMTs3H0CMKFB3ci0+w6cG/dYiVx3HVxySao8ZgycpO/WikjbiJtkXgCOB+4BHgKeICy9/K9kwpJW+/TTxuu6aEJLEWljsT5x3P14d78nKl5KWAXzTuDEhOKS1jjvvMwEM3GiJrQUkYKI25LZyN3rgDEJxCKt9dFH0Lt3qty9e5iiX0SkQOIOYd4SOB/YF9g8fZu7fyuBuKSljjkGxo9PladMgf32K1w8IiLEb8n8nTBy62FAXwMuJu+/D7ulfZ9h333h9dcLF4+ISJq4SWZ/oKcWKisyAwfCjBmp8gcfwI75moRBRKT14vYEvwgMTDIQaYHJk8M3OusTzPe+Fzr2lWBEpMjEbcmcCkwws8nAx+kb3D3WkgCSB+5hdcq6tElFFi+GBOZhExHJh7gtmasJ84NtB+ySdts5obikoccfD0OQ6xPMz34Wko4SjIgUsbgtmROAXaO5wqQt1dVBeYM5SD/7LAxPFhEpcnFbMrMIU8pIWxo9OjPB3HRTaL0owYhIiYjbkhkDPGpmt9C4T0ZTy+TbunWw2WaZdevXQ0X22Z1FRIpV3JbMuUBv4LfA3Wm3uxKKq+P63//NTDAPPBBaL0owIlKCYrVk8jWdvzRh5Uro0SOzrq6ucIvPiIjkgWZMLAZnnZWZYJ55JrRelGBEpMS1eIJMyaMFC2CHHVLlXr1gyZLCxSMikmdqyRTKYYdlJpg33lCCEZF2Ry2ZtjZ9OuyxR6q8//7wyiuFi0dEJEFKMm1pwACYMydVnj0bKisLFY2ISOJ0uawtvPRS6MSvTzDDh4eOfSUYEWnnCt6SMbOtgb8ClcAc4Hh3X95gn37AvcD2QB0wyt1vjrZdAZwJ1HdoXOruE9oi9mZlW/J46VLo2bMw8YiItLFiaMlcAjzj7rsAz0TlhjYAF7j7QMLaNueaWVrHBje5+z7RrTgSzLhxmQnmkktC0lGCEZEOpOAtGWAYcEh0/8/As8DF6TtEE3Muiu6vMrPpQF/g3TaLMq7a2jAdf7rVq6Fbt8LEIyJSQMXQktmufnbn6Oe2Te1sZpXAvsDktOrzzOwtMxttZls1cewIM6s2s+olSQwXHjkyM8HcemtovSjBiEgH1SYtGTP7J6E/paFftvBxNgf+AfyPu38aVd8O/Abw6OfvgdOzHe/uo4BRAFVVVd6S527S2rWNE0lNTeMWjYhIB9MmLRl3/6a775Xl9gjwsZn1Boh+Ls72GGZWQUgw97n72LTH/tjda929DrgTGJT8GaW5/PLMBPPQQ6kVLEVEOrhi+CR8FDgFuDb6+UjDHczMCLM+T3f3Gxts6522mNp3gHeSDTeyfDlsvXVmnSa0FBHJUAx9MtcCh5nZf4DDojJm1sfM6keKHQj8CPiGmb0R3Y6Mtv3OzN42s7eAIcBPE4/4lFMyE8xzz2lCSxGRLAreknH3ZcChWeoXAkdG918Esn6Cu/uPEg2woR13DN/UB+jbF+bPb9OnFxEpJcXQkiktw4aFn++8owQjItIMJZmWuummcGlszz0LHYmISNFTkhERkcQoyYiISGKUZEREJDFKMiIikhglGRERSYySjIiIJEZJRkREEqMkIyIiiTH3/M14X0rMbAkwdxMP7wUszWM4hdbezgfa3znpfIpfezunXOfzRXffJu6DdNgk0xpmVu3uVYWOI1/a2/lA+zsnnU/xa2/nlK/z0eUyERFJjJKMiIgkRklm04wqdAB51t7OB9rfOel8il97O6e8nI/6ZEREJDFqyYiISGKUZEREJDFKMg2Y2VAze8/MZprZJVm2m5n9X7T9LTPbL+6xhdDK85ljZm+b2RtmVt22kWcX43x2N7NXzGydmV3YkmMLoZXnU3SvD8Q6pxOj99pbZvayme0d99hCaOX5FN1rFON8hkXn8oaZVZvZ1+Iem5W76xbdgHLgA2BHoDPwJrBHg32OBJ4ADNgfmBz32FI6n2jbHKBXoV+XFp7PtsB/AVcDF7bk2FI6n2J8fVpwTgcAW0X3j2gHf0NZz6cYX6OY57M5qf76LwMzWvP6qCWTaRAw091nuft64EFgWIN9hgH3ejAJ6GFmvWMe29Zacz7FqNnzcffF7v4aUNPSYwugNedTrOKc08vuvjwqTgJ2iHtsAbTmfIpRnPP5zKOsAnQHPO6x2SjJZOoLzEsrz4/q4uwT59i21przgfDmesrMppjZiMSijK81v+NSfX2aUmyvD7T8nH5MaElvyrFtoTXnA8X3GsU6HzP7jpnNAB4HTm/JsQ112uRQ2yfLUtdwjHeufeIc29Zacz4AB7r7QjPbFnjazGa4+/N5jbBlWvM7LtXXpynF9vpAC87JzIYQPpTrr/mX9GuU5Xyg+F6jWOfj7g8DD5vZQcBvgG/GPbYhtWQyzQf6pZV3ABbG3CfOsW2tNeeDu9f/XAw8TGguF1Jrfsel+vrkVISvD8Q8JzP7MnAXMMzdl7Xk2DbWmvMpxteoRb/jKCHuZGa9Wnps+oPolurw6gTMAgaQ6tjas8E+R5HZUf5q3GNL7Hy6A1uk3X8ZGFrs55O27xVkdvyX5OvTxPkU3evTgvdcf2AmcMCm/j5K5HyK7jWKeT47k+r43w9YEH0+bNLrU9A3ZDHeCKOt3ieMovhlVHc2cHZ034Dbou1vA1VNHVvo26aeD2EEyZvRbVoJnc/2hP+4PgVWRPe/UMKvT9bzKdbXJ+Y53QUsB96IbtVNHVvo26aeT7G+RjHO5+Io3jeAV4Cvteb10bQyIiKSGPXJiIhIYpRkREQkMUoyIiKSGCUZERFJjJKMiIgkRklGRFrNzKaZ2SGFjkOKj5KMdGhmdqqZvVjoOEqdu+/p7s/G2Tea/v6bCYckRUJJRtq1aL0cvc9FCkR/fFJUzOw0M3ssrTzTzP6WVp5nZvuY2QFm9pqZrYx+HpC2z7NmdrWZvQSsAXaMWiyzzGyVmc2OFpoaCIwEvmpmn5nZimZi62pmvzezudHzvmhmXaNtx0aXjFZEzz8w7bg5ZnZhtBDUSjP7q5ltlrZ9WLRA1Kdm9oGZDU37XUyPYp5lZmelHTPdzI5OK3cys6UWLTpnZvtHC2itMLM341zKiuK+xsxejeJ8xMy2Ttve3Dl+M7p/hZn9zczujWKfZmZV0bYxhGlYHot+5z83s83M7C9mtix67NfMbLvm4pUSUegpDnTTLf1GmIpjBeEfoN7AXGBB2rblwNbRzx8R5lMaHpV7Rvs9C3wI7Blt35IwLctu0fbeRHMuAacCL8aM7bbosfsSFnA6AOgC7AqsBg4DKoCfE+ay6hwdNwd4FegTxT6d1BQeg4CV0bFl0WPvHm07CtiJMPXPwYSEuV+07XLgvrTYjiK1uFRfYBlhCpCy6LGXAds0c37PEuap2osw19Y/gL9E2+Kc4zej+1cAn0fPXw5cA0xKe56N+0bls4DHgG7R/l8hmgpIt9K/qSUjRcXdZwGrgH0IH6wTgQVmtntUfoHwgfofdx/j7hvc/QFgBnBM2kPd4+7T3H0DsAGoA/Yys67uvsjdp7UkruiS2+nA/3P3Be5e62GxqnXAD4DH3f1pd68BbgC6EpJQvf9z94Xu/gnhA3WfqP7HwOjo2LrosWdEv4vH3f0DD54DngK+Hh13P3CsmXWLyj+M6gBOAia4+4ToMZ8Gqgkf+s0Z4+7vuPtq4FfA8WZWHvMc070YPX8tMAbYu4nnrAF6AjtHv9cp7v5pjFilBCjJSDF6DjgEOCi6/ywhwRwclfsQWjjp5pK5gNLGxZWiD8wfECYBXGRmj0dJqyV6AZsRJgZsKCMed6+Lnj89no/S7q8hLHELYer0bI+JmR1hZpPM7JPoUt6RURy4+0xCi+iYKNEcSyrJfBE4Lrr0tCI69muEFlxz0helmktotfSKeY7pGp7vZmaWa/2qMYR/Jh40s4Vm9jszq4gRq5QAJRkpRvVJ5uvR/efITDILCR+k6foTLvXUy5j51d0nuvthhA/aGcCd2fZrwlLCJaCdsmzLiMfMjJA8FmTZt6F52R7TzLoQLlfdAGzn7j2ACWQuHPUA4VLhMODdKPHUP+YYd++Rduvu7tfGiCd9vZD+hFbG0laeY0MNX5sad7/S3fcgtIyOBk7ehMeVIqQkI8XoOWAI0NXd5xMukQ0lXFKZSviw3dXMfhh1eP8A2AMYn+3BzGy7qNO6O7AO+AyojTZ/DOxgZp2bCij6z300cKOZ9TGzcjP7apQM/gYcZWaHRv+BXxA9z8sxzvVu4LTo2DIz6xu1sjoT+nuWABvM7AjgWw2OfTCqO4dUKwbgL4QWzuFRnJuZ2SFmFmft+ZPMbI+odXQV8FB0yas159jQx4T+NSCsKGlmX4ouy31KSGy1uQ6W0qIkI0XH3d8nJIIXovKnhMWSXoqu2S8j/Ld7AaFD++fA0e6+NMdDlkX7LgQ+IbSIfhJt+xdh7YyPzCzX8fUuJKy581r0ONcBZe7+HqEf5BbCf/3HAMe4+/oY5/oqcBpwE2EAwHPAF919FXA+4cN9OaHP5dEGxy4irPdxAPDXtPp5hNbNpYQkNQ+4iHh/72OAewiXuzaLYqA155jFNcBl0aW8Cwlr5jxESDDTCb+Dv2zC40oR0noyIgKEIcyE0WR3FToWaT/UkhERkcTkGu0h0iGZ2TQaDyoAOMvd72vrePLNzD7LsemINg1EOgxdLhMRkcTocpmIiCRGSUZERBKjJCMiIolRkhERkcQoyYiISGL+P16KmFwo+cKiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a linear regression instance\n",
    "lr = LinearRegression()\n",
    "\n",
    "# Training the model on the data, storing the information learned from the data\n",
    "# Model is learning the relationship between X and y \n",
    "lr.fit(X,y)\n",
    "\n",
    "# Get Predictions for original x values\n",
    "# This is not how we will do it for the rest of the course.\n",
    "predictions = lr.predict(X)\n",
    "\n",
    "plt.scatter(df['worst_concave_points'], df['diagnosis'])\n",
    "plt.plot(df['worst_concave_points'], predictions, color='red')\n",
    "\n",
    "\n",
    "plt.ylabel('malignant (1) or benign (0)', fontsize = 12)\n",
    "plt.xlabel('worst_concave_points', fontsize = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For now, around prediction value (red) >= 0.5 (around .15 for worst_concave_point), we predict a class of 1 (malignant), else we predict a class of 0 (benign)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem: If the value for worse_concave_points is .0, what does it mean when we have -.25 for our class instead of a 1 or zero? This seems odd. Maybe we should constrain our predictions between 0 and 1. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is Logistic Regression\n",
    "Linear regression: Continuous response is modeled as a linear combination of the features.\n",
    "\n",
    "$$y = \\beta_0 + \\beta_1x$$\n",
    "\n",
    "Logistic Regression: Bound output to 0 and 1. This will make logistic regression output the probabilities of a specific class. Probabilities can be converted into class predictions\n",
    "\n",
    "$$y = \\frac{1} {1 + e^{-(\\beta_0 + \\beta_1x)}}$$\n",
    "\n",
    "This is graphed below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7c780408b0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sigmoid(x):\n",
    "    a = []\n",
    "    for item in x:\n",
    "        a.append(1/(1+np.exp(-item)))\n",
    "    return(a)\n",
    "\n",
    "x = np.arange(-10., 10., 0.2)\n",
    "sig = sigmoid(x)\n",
    "\n",
    "plt.plot(x, sig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Showing Predictions for Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1000)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df['worst_concave_points'].values.reshape(-1,1)\n",
    "y = df['diagnosis']\n",
    "\n",
    "logreg = LogisticRegression(C = 1000)\n",
    "\n",
    "# Training the model on the data, storing the information learned from the data\n",
    "# Model is learning the relationship between X and y \n",
    "logreg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'worst_concave_points')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_df = pd.DataFrame(data = {'worst_concave_points': df['worst_concave_points'].values,\n",
    "                     'diagnosis': df['diagnosis'].values})\n",
    "\n",
    "example_df['logistic_preds'] = pd.DataFrame(logreg.predict_proba(X))[1]\n",
    "example_df = example_df.sort_values(['logistic_preds'])\n",
    "\n",
    "plt.scatter(example_df['worst_concave_points'], example_df['diagnosis'])\n",
    "plt.plot(example_df['worst_concave_points'], example_df['logistic_preds'].values, color='red')\n",
    "\n",
    "plt.ylabel('malignant (1) or benign (0)', fontsize = 12)\n",
    "plt.xlabel('worst_concave_points', fontsize = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>worst_concave_points</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>logistic_preds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.2733</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>0.2756</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.2867</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.2903</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>0.2910</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     worst_concave_points  diagnosis  logistic_preds\n",
       "568                0.0000          0        0.000322\n",
       "314                0.0000          0        0.000322\n",
       "473                0.0000          0        0.000322\n",
       "538                0.0000          0        0.000322\n",
       "192                0.0000          0        0.000322\n",
       "..                    ...        ...             ...\n",
       "202                0.2733          1        0.999795\n",
       "352                0.2756          1        0.999822\n",
       "82                 0.2867          1        0.999909\n",
       "181                0.2903          1        0.999927\n",
       "108                0.2910          1        0.999930\n",
       "\n",
       "[569 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this is unclear, check out the visualization below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is just for the youtube thumbnail so that the black text has a white background. \n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (16,9), facecolor='white');\n",
    "\n",
    "\n",
    "malignantFilter = example_df['diagnosis'] == 1\n",
    "benignFilter = example_df['diagnosis'] == 0\n",
    "\n",
    "ax.scatter(example_df.loc[malignantFilter, 'worst_concave_points'].values,\n",
    "            example_df.loc[malignantFilter, 'logistic_preds'].values,\n",
    "           color = 'g',\n",
    "           s = 110,\n",
    "           alpha = .8,\n",
    "           label = 'malignant')\n",
    "\n",
    "\n",
    "ax.scatter(example_df.loc[benignFilter, 'worst_concave_points'].values,\n",
    "            example_df.loc[benignFilter, 'logistic_preds'].values,\n",
    "           color = 'b',\n",
    "           s = 110,\n",
    "           alpha = .8,\n",
    "           label = 'benign')\n",
    "\n",
    "ax.axhline(y = .5, c = 'y')\n",
    "\n",
    "ax.axhspan(.5, 1, alpha=0.05, color='green')\n",
    "ax.axhspan(0, .4999, alpha=0.05, color='blue')\n",
    "ax.text(0.2, .6, 'Classified as malignant', fontsize = 24)\n",
    "ax.text(0.2, .4, 'Classified as benign', fontsize = 24)\n",
    "\n",
    "ax.set_ylim(0,1)\n",
    "ax.legend(loc = 'lower right', markerscale = 1.0, fontsize = 24)\n",
    "ax.tick_params(labelsize = 18)\n",
    "ax.set_xlabel('worst_concave_points', fontsize = 30)\n",
    "ax.set_ylabel('probability of malignant', fontsize = 30)\n",
    "ax.set_title('Logistic Regression Predictions', fontsize = 48)\n",
    "\n",
    "fig.tight_layout()\n",
    "#fig.savefig('LogisticRegressionPredictions.png', dpi = 950)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Advantages of logistic regression:</b>\n",
    "\n",
    "Able to interpret how the model makes predictions\n",
    "\n",
    "Model training and prediction are relatively fast\n",
    "\n",
    "No tuning is usually needed (excluding regularization)\n",
    "\n",
    "Can perform well with a small number of observations\n",
    "\n",
    "Outputs well-calibrated predicted probabilities\n",
    "\n",
    "<b>Disadvantages of logistic regression:</b>\n",
    "\n",
    "Presumes a linear relationship between the features and the log odds of the response\n",
    "\n",
    "Performance is usually not competitive with the best supervised learning methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation Metrics\n",
    "\n",
    "We have previously used accuracy to assess how good our classifier was for decision trees. This is a common classification metric across classification models. \n",
    "\n",
    "Accuracy is defined as:\n",
    "\n",
    "(fraction of correct predictions): correct predictions / total number of data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9068541300527241\n"
     ]
    }
   ],
   "source": [
    "score = logreg.score(X, y)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is one metric, but it doesn't say give much insight into what was wrong. We also previously looked into a confusion matrix. Let's look at this in more detail before getting into new topics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = metrics.confusion_matrix(y, logreg.predict(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.5, -0.5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(cm, annot=True,\n",
    "            fmt=\".0f\",\n",
    "            linewidths=.5,\n",
    "            square = True,\n",
    "            cmap = 'Blues');\n",
    "plt.ylabel('Actual label', fontsize = 17);\n",
    "plt.xlabel('Predicted label', fontsize = 17);\n",
    "plt.title('Accuracy Score: {}'.format(score), size = 17);\n",
    "plt.tick_params(labelsize= 15)\n",
    "\n",
    "# You can comment out the next 4 lines if you like\n",
    "b, t = plt.ylim() # discover the values for bottom and top\n",
    "b += 0.5 # Add 0.5 to the bottom\n",
    "t -= 0.5 # Subtract 0.5 from the top\n",
    "plt.ylim(b, t) # update the ylim(bottom, top) values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the same information in a table in another manner. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ignore this code\n",
    "\n",
    "modified_cm = []\n",
    "for index,value in enumerate(cm):\n",
    "    if index == 0:\n",
    "        modified_cm.append(['TN = ' + str(value[0]), 'FP = ' + str(value[1])])\n",
    "    if index == 1:\n",
    "        modified_cm.append(['FN = ' + str(value[0]), 'TP = ' + str(value[1])])   \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.5, -0.5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(cm, annot=np.array(modified_cm),\n",
    "            fmt=\"\",\n",
    "            annot_kws={\"size\": 20},\n",
    "            linewidths=.5,\n",
    "            square = True,\n",
    "            cmap = 'Blues',\n",
    "            xticklabels = ['Benign', 'Malignant'],\n",
    "            yticklabels = ['Benign', 'Malignant'],\n",
    "            );\n",
    "\n",
    "plt.ylabel('Actual label', fontsize = 17);\n",
    "plt.xlabel('Predicted label', fontsize = 17);\n",
    "plt.title('Accuracy Score: {:.3f}'.format(score), size = 17);\n",
    "plt.tick_params(labelsize= 15)\n",
    "\n",
    "# You can comment out the next 4 lines if you like\n",
    "b, t = plt.ylim() # discover the values for bottom and top\n",
    "b += 0.5 # Add 0.5 to the bottom\n",
    "t -= 0.5 # Subtract 0.5 from the top\n",
    "plt.ylim(b, t) # update the ylim(bottom, top) values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>True negatives (TN):</b> We predicted benign (no) and the cancer is actually benign (no). Model **does not** predict a case (and the case **is not** true in the data)\n",
    "\n",
    "<b>False positives (FP):</b> We predicted malignant (yes) and the cancer is actually benign (no). Model **predicts** a case (and the case **is not** true in the data)\n",
    "\n",
    "<b>False negatives (FN):</b> We predicted benign (no) and the cancer is actually malignant (yes). Model **does not** predict a case (and the case **is true** in the data)\n",
    "\n",
    "<b>True positives (TP):</b> We predicted malignant (yes) and the cancer is actually malignant (yes). Model **predicts** a case (and the case **is true** in the data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using those values, we can compute the **sensitivity** and **specificity** of our model:\n",
    "\n",
    "\\begin{equation*}\n",
    "Sensitivity = \\frac { True Positives }{ True Positives+False Negatives } \n",
    "\\end{equation*}\n",
    "\n",
    "\\begin{equation*}\n",
    "Specificity = \\frac { TrueNegatives }{ TrueNegatives+FalsePositives } \n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity: 0.868\n",
      "Specificity: 0.930\n"
     ]
    }
   ],
   "source": [
    "true_pos = cm[1,1]\n",
    "false_pos = cm[0,1]\n",
    "true_neg = cm[0,0]\n",
    "false_neg = cm[1,0]\n",
    "\n",
    "# Calculate Sensitivity, specificity\n",
    "sensitivity = true_pos / (true_pos + false_neg)\n",
    "specificity = true_neg / (true_neg + false_pos)\n",
    "\n",
    "print('Sensitivity: {:.3f}'.format(sensitivity))\n",
    "print('Specificity: {:.3f}'.format(specificity))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sensitivity**, also referred to as the true positive rate, tells us, of all of the **cases in the data**, how many did we accurately predict? This indicates the model's **ability to detect cases**. In other words, how **sensitively** does the model pick up on cases?\n",
    "\n",
    "**Specificity**, also referred to as the true negative rate, tells us, of all of the **non-cases in the data**, how many did we accurately predict? This indicates the model's ability to assign non-cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type 1 Error Rate: 0.070\n",
      "Type 2 Error Rate: 0.132\n"
     ]
    }
   ],
   "source": [
    "type_one_error = 1 - specificity\n",
    "type_two_error = 1 - sensitivity\n",
    "print('Type 1 Error Rate: {:.3f}'.format(type_one_error))\n",
    "print('Type 2 Error Rate: {:.3f}'.format(type_two_error))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These metrics are directly used to calculate **Type I and Type II error rate**, which are analagous to Type I and Type II errors in statistical tests. \n",
    "\n",
    "> **Type I Error** rate is the proportion of instances which are **incorrectly classified as positive cases** (relative to the total number of **negative cases**). It is calculated as $1-specificity$, or simply the false positives relative to the total non-cases in the data, $FP/N$.\n",
    "\n",
    "> **Type II Error** rate is the proportion of instances which are **incorrectly classified as negative cases** (relative to the total number of **positive cases**). It is calculated as $1-sensitivity$, or simply the false negatives relative to the total cases in the data, $FN/P$.\n",
    "\n",
    "Part of this lecture was modified from [Michael Freeman's work](https://github.com/mkfreeman)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Understanding\n",
    "\n",
    "#### Question\n",
    "Give an example when we care about sensitivity (true positive rate), but not as much about specificity (true negative rate).\n",
    "\n",
    "#### Answer\n",
    "If we are diagnosing cancer we prefer to have false positives, predict a cancer when there is no cancer, that can be later corrected with a more specific test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question\n",
    "Give an example when we care about specificity (true negative rate), but not as much about sensitivity (true positive rate).\n",
    "\n",
    "#### Answer\n",
    "\n",
    "If we are doing spam detection, we want to be precise. Anything that we remove from an inbox must be spam, which may mean accepting fewer true positives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trading True Positives and True Negatives\n",
    "\n",
    "By default, and with respect to the underlying assumptions of logistic regression, we predict a positive class when the probability of the class is greater than .5 and predict a negative class otherwise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question\n",
    "\n",
    "What if we decide to use .2 as a threshold for picking the positive class? \n",
    "\n",
    "We will predict more positive classes, but fewer true negatives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ROC Curve\n",
    "It is common to compare the _true positive rate_ (sensitivity) to the _false positive rate_ (1 - specificity) at each **threshold** for classification in an ROC Curve.\n",
    "\n",
    "* Useful to help choose a threshold that appropriately balances sensitivity and specificity.\n",
    "* Harder to use when there are more than two classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate data for the ROC curve using the `metrics.roc_curve` function\n",
    "fpr, tpr, thresholds  = metrics.roc_curve(example_df['diagnosis'], example_df['logistic_preds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw your ROC curve\n",
    "plt.figure(figsize=(9,9))\n",
    "plt.title(\"ROC Curve for the model\", fontsize = 17)\n",
    "plt.plot(fpr, tpr)\n",
    "plt.plot(fpr, fpr, 'b--')\n",
    "plt.xlabel('False Positive Rate', fontsize = 17)\n",
    "plt.ylabel('True Positive Rate', fontsize = 17)\n",
    "plt.tick_params(labelsize= 15)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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